code: def test_tf_dpcl_loss_criterion_forward(loss_type):
      criterion = FrequencyDomainDPCL(loss_type=loss_type)
  
      batch = 2
      inf = torch.rand(batch, 10 * 200, 40)
      ref_spec = [
          ComplexTensor(
              torch.rand(batch, 10, 200), torch.rand(batch, 10, 200)
          ),
          ComplexTensor(
              torch.rand(batch, 10, 200), torch.rand(batch, 10, 200)
          ),
          ComplexTensor(
              torch.rand(batch, 10, 200), torch.rand(batch, 10, 200)
          ),
      ]
  
      ref = [abs(r) for r in ref_spec]
  
      loss = criterion(ref, inf)
      assert loss.shape == (batch,), (
          "Invalid loss shape with " + criterion.name
      )
  
file_path: espnet/test/espnet2/enh/loss/criterions/test_tf_domain.py
imports: import pytest
  import torch
  from packaging.version import parse
  from torch_complex import ComplexTensor
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainAbsCoherence, FrequencyDomainCrossEntropy, FrequencyDomainDPCL, FrequencyDomainL1, FrequencyDomainMSE
label_desc: Use image node_id 5 for calling a global function with example usage: test_tf_dpcl_loss_criterion_forward(loss_type) without return types